Test difference between means of entire collection (EC) and core set (CS) for quantitative traits by Student's t test (Student 1908) .
ttest.evaluate.core(data, names, quantitative, selected)The data as a data frame object. The data frame should possess one row per individual and columns with the individual names and multiple trait/character data.
Name of column with the individual names as a character string.
Name of columns with the quantitative traits as a character vector.
Character vector with the names of individuals selected in
core collection and present in the names column.
The quantitative trait.
The minimum value of the trait in EC.
The maximum value of the trait in EC.
The mean value of the trait in EC.
The standard error of the trait in EC.
The minimum value of the trait in CS.
The maximum value of the trait in CS.
The mean value of the trait in CS.
The standard error of the trait in CS.
The p value of the Student's t test for equality of means of EC and CS.
The significance of the Student's t test for equality of means of EC and CS.
Student (1908). “The probable error of a mean.” Biometrika, 6(1), 1–25.
data("cassava_CC")
data("cassava_EC")
ec <- cbind(genotypes = rownames(cassava_EC), cassava_EC)
ec$genotypes <- as.character(ec$genotypes)
rownames(ec) <- NULL
core <- rownames(cassava_CC)
quant <- c("NMSR", "TTRN", "TFWSR", "TTRW", "TFWSS", "TTSW", "TTPW", "AVPW",
"ARSR", "SRDM")
qual <- c("CUAL", "LNGS", "PTLC", "DSTA", "LFRT", "LBTEF", "CBTR", "NMLB",
"ANGB", "CUAL9M", "LVC9M", "TNPR9M", "PL9M", "STRP", "STRC",
"PSTR")
ec[, qual] <- lapply(ec[, qual],
function(x) factor(as.factor(x)))
ttest.evaluate.core(data = ec, names = "genotypes",
quantitative = quant, selected = core)
#> Trait EC_Min EC_Max EC_Mean EC_SE CS_Min CS_Max CS_Mean CS_SE
#> 1 NMSR 1.00 55.00 11.722090 0.18731651 1.00 55.00 10.892857 0.6314310
#> 2 TTRN 0.25 13.75 3.853909 0.04656232 0.25 13.75 3.930655 0.1754066
#> 3 TFWSR 0.00 40.00 5.428979 0.11045567 0.20 38.00 6.348214 0.4696134
#> 4 TTRW 0.00 20.20 1.897948 0.04037796 0.10 20.20 2.617817 0.2207393
#> 5 TFWSS 0.20 42.00 6.943052 0.14359496 0.20 42.00 7.748214 0.5588314
#> 6 TTSW 0.04 22.00 2.387502 0.04878843 0.10 22.00 3.069087 0.2381436
#> 7 TTPW 0.40 80.00 12.372031 0.23851356 0.40 80.00 14.096429 0.9720528
#> 8 AVPW 0.20 33.00 4.285450 0.08195995 0.20 33.00 5.686905 0.4210897
#> 9 ARSR 0.00 18.00 1.858076 0.05493488 0.00 8.00 1.702381 0.1516377
#> 10 SRDM 0.50 48.90 37.771021 0.12264395 21.90 48.10 37.730357 0.3289992
#> ttest_pvalue ttest_significance
#> 1 0.209505833 ns
#> 2 0.672855738 ns
#> 3 0.058266745 ns
#> 4 0.001585008 **
#> 5 0.164507433 ns
#> 6 0.005599998 **
#> 7 0.086559633 ns
#> 8 0.001302429 **
#> 9 0.335457319 ns
#> 10 0.907907091 ns